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Multi-Agent RAG System

Precision
Cooking.

Explore The Low-Cost Cookbook with AI-powered memory and intelligent multi-turn conversations.

How It Works

Multi-agent system that routes, retrieves, evaluates, and remembers.

Router

Determines if the question is cookbook-related, a catalog request, or out-of-scope. Handles follow-ups intelligently.

Retrieval

Searches the cookbook using semantic vector search (Chroma). Returns relevant recipe chunks with metadata (source, page).

LLM

Synthesizes an answer using ONLY the retrieved cookbook context. Supports streaming tokens for real-time UX.

Evaluation

Verifies the answer is supported by retrieved chunks. Produces confidence scores and facts-checked metadata.

Memory

Persists chat messages and recipe context in SQLite. Enables follow-ups and maintains session history.

Response

Returns the final answer with sources (full book name + page), evaluation metrics, and structured metadata.

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Engineering Team

Walid Alsafadi

Walid Alsafadi

RAG & Backend Lead

Fares Alnamla

Fares Alnamla

AI Agent Engineer

Ahmed Alyazuri

Ahmed Alyazuri

Frontend Developer